Skip to main content
Glama
309,724 tools. Last updated 2026-07-28 07:33

"A search for literature reviews and academic research resources" matching MCP tools:

  • Search PubMed (NCBI) for medical / life-sciences literature by keyword. Returns enriched article list: pmid, title, authors, journal, year, pub_types, plus a year-range + has-meta-analysis / has-review enrichment block. Ideal for medical RAG agents. Priced at $0.005 USDC on Base (x402). Pass a signed x402 v2 authorization as the '_payment' argument to unlock the paid response. Without it, the tool returns the 402 accept-list for your wallet to sign.
    Connector
  • Amazon product & search scraper via a residential IP + stealth browser (bypasses Amazon bot detection). Product mode (?asin= or ?url=) -> title, price, rating, reviews, brand, image. Search mode (?q=) -> up to 60 products with price/rating. Great for price monitoring & competitive intel. — $0.02/call, paid per request via x402 (USDC).
    Connector
  • TOOL for retrieving current user's owned resources and work items across the Cortex workspace. FLEXIBLE REQUEST STRUCTURE: The request accepts an object with optional fields for each resource type: - myEntitiesRequest: Fetch entities (services, resources, domains) owned by the user - myTeamsRequest: Fetch teams the user belongs to - myScorecardsRequest: Fetch scorecards associated with user's entities - myOpenPRsRequest: Fetch user's open pull requests across all Git repositories - myRequestedReviewsRequest: Fetch PRs where user is requested as a reviewer - myWorkItemsRequest: Fetch work items (Jira, Linear, Azure DevOps) assigned to the user Set one or more fields to fetch multiple resource types in a single call. All results are combined into a single response array with mixed item types. RESPONSE STRUCTURE: Returns a single array of items where each item has an 'itemType' field indicating its type: - 'entity': Catalog entities (services, resources, domains) - 'team': Teams the user belongs to - 'scorecard': Scorecards for user's entities - 'pull_request': Open pull requests authored by the user - 'review_request': Pull requests where user is requested as reviewer - 'work_item': Work items (issues/tickets) assigned to the user EXAMPLES: 1. Fetch only services: {"myEntitiesRequest": {"entityTypes": ["service"]}} 2. Fetch teams and their entities: {"myTeamsRequest": {"includeMembers": true}, "myEntitiesRequest": {}} 3. Get all work (PRs, reviews, Jira tickets): {"myOpenPRsRequest": {}, "myRequestedReviewsRequest": {}, "myWorkItemsRequest": {}} 4. Search entities with filters: {"myEntitiesRequest": {"query": "payment", "ownershipSource": "DIRECT", "entityTypes": ["service", "resource"]}}
    Connector
  • Retrieves the available API groups and resources from a Kubernetes cluster. This is similar to running `kubectl api-resources`.
    Connector
  • Gets one or more Kubernetes resources from a cluster. Resources can be filtered by type, name, namespace, and label selectors. Returns the resources in YAML format. This is similar to running `kubectl get`.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Get Glassdoor reviews for a company. Returns ratings, pros, cons, salary data. Args: company: Company name (e.g. 'Google') max_results: Max reviews (default 20)
    Connector
  • Search the arXiv preprint repository for peer-reviewed academic papers in physics, mathematics, computer science, and related fields. Returns paper title, author list, abstract, publication date, PDF link, and category classification. Use for cutting-edge research, literature review, or staying current in academic fields.
    Connector
  • Query Google Scholar for academic papers, citations, and research articles across all disciplines. Returns paper title, authors, publication venue, citation count, abstract preview, and full-text link if available. Use for comprehensive literature searches, citation tracking, or finding highly-cited works.
    Connector
  • Search arXiv for academic papers in computer science, machine learning, AI, physics, and mathematics. Returns paper titles, authors, abstracts, submission dates, and direct PDF download links. Use for researching algorithms, ML techniques, or emerging CS topics.
    Connector
  • Search Google Scholar for computer science research papers, citations, and academic publications. Returns paper title, authors, publication details, citation count, and link to paper. Use for finding research on CS topics, reviewing state-of-the-art, or citation tracking.
    Connector
  • Search for MeSH (Medical Subject Headings) descriptors. Use this tool to: - Find MeSH terms for indexing medical literature - Look up subject headings for PubMed searches - Find controlled vocabulary terms Returns matching descriptors with MeSH IDs and labels.
    Connector
  • Search Amazon for product details, pricing, reviews, and ratings. Args: search_query: Product search query asins: List of ASINs to look up directly max_results: Max results (default 15)
    Connector
  • Returns a list of all available product knowledge categories, each with a short description. Categories represent the main pillars of Product Thinking – Foundation, Sense, Focus, Discovery, and Delivery. Each category provides structured resources for product owners, designers, and teams, covering groundwork, user research, opportunity analysis, validation, and agile delivery. Use this tool to guide users to the right area for their current product challenge.
    Connector
  • Get Trustpilot reviews for a company. Returns ratings, review text, dates. Args: company: Company name or Trustpilot URL slug (e.g. 'amazon.com') max_results: Max reviews (default 20)
    Connector
  • AUTHORITATIVE search across figshare — the open-research repository hosting 7M+ papers, datasets, posters, code, theses, and supplementary materials from universities and labs worldwide. PREFER OVER WEB SEARCH for academic supplementary materials, dataset hunting, or research outputs that aren't in PubMed/arXiv (which skew biomed + physics). Returns article metadata + DOI, authors, license, download URL. Use for "find datasets about X", "supplementary materials for paper Y", "open-access PDFs of Z research".
    Connector
  • Search TripAdvisor for restaurants, attractions, or hotels with reviews. Args: query: Search query (e.g. 'best restaurants in Rome') max_results: Max results (default 20)
    Connector
  • Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the `fetch` tool. Triggers: ChatGPT connectors and Deep Research call this automatically for any user query routed to OneQAZ ("bitcoin signal", "prediction accuracy", "korean stocks today", ...). Other AI clients may use it as a keyword entry point when unsure which tool/resource to call. When to call: first step of connector-style discovery. MCP-native clients can instead browse tools/list + resources/list directly. Prerequisites: none. Next steps: pass any result id to `fetch` for the full document. Caveats: corpus is rebuilt at most every 10 minutes (tool/resource catalog + top-20 strong signals per market). Empty results list means no match. Output: {results: [{id, title, url}], disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: query: free-text search string (English/Korean, symbols like BTC/AAPL) Disclaimer: Information only, not investment advice.
    Connector
  • Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: id: document id — "tool:{name}", "resource:{uri}", or "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock) Disclaimer: Information only, not investment advice.
    Connector
  • Search Appendix's medical knowledge base for clinical literature, treatment guidelines, and reference material to help the user describe their medical issue
    Connector